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README.md

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+---
+language: en
+tags:
+- medical-image-segmentation
+- pytorch
+- u-rwkv
+datasets:
+- rwkv
+metrics:
+- dice
+model-index:
+- name: u-rwkv-polyp
+  results:
+  - task: 
+      type: image-segmentation
+      name: Medical Image Segmentation
+    dataset:
+      type: rwkv
+      name: RWKV
+    metrics:
+      - type: dice
+        value: 0.7887
+---
+
+# u-rwkv-polyp
+
+This model is part of the U-RWKV family of medical image segmentation models. It combines the power of RWKV (Receptance Weighted Key Value) attention mechanism with U-Net architecture for efficient and accurate medical image segmentation.
+
+## Model description
+
+U-RWKV model trained on Polyp dataset for polyp segmentation
+
+### Architecture
+
+- Base architecture: U-Net with RWKV attention
+- Input channels: 3
+- Output channels: 1
+- Base channels: [16, 32, 128, 160, 256]
+- Attention mechanism: RWKV (Receptance Weighted Key Value)
+- Feature fusion: SE (Squeeze-and-Excitation)
+
+## Performance
+
+- Dice score: 0.7887
+
+## Usage
+
+```python
+import torch
+from models.model import U_RWKV
+
+# Load model
+model = U_RWKV()
+checkpoint = torch.load('model.pth')
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# Inference
+with torch.no_grad():
+    output = model(input_image)
+```
+
+## Training
+
+The model was trained using:
+- Loss functions: Dice Loss + BCE Loss
+- Optimizer: AdamW
+- Learning rate: 1e-4
+- Batch size: 16
+- Data augmentation: Random flip, rotation, scaling
+
+## License
+
+This model is released under the MIT License.

model.pth

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